89 research outputs found
Local random potentials of high differentiability to model the Landscape
We generate random functions locally via a novel generalization of Dyson
Brownian motion, such that the functions are in a desired differentiability
class, while ensuring that the Hessian is a member of the Gaussian orthogonal
ensemble (other ensembles might be chosen if desired). Potentials in such
higher differentiability classes are required/desirable to model string
theoretical landscapes, for instance to compute cosmological perturbations
(e.g., smooth first and second derivatives for the power-spectrum) or to search
for minima (e.g., suitable de Sitter vacua for our universe). Since potentials
are created locally, numerical studies become feasible even if the dimension of
field space is large (D ~ 100). In addition to the theoretical prescription, we
provide some numerical examples to highlight properties of such potentials;
concrete cosmological applications will be discussed in companion publications.Comment: V2: added discussion section to match published version (conclusions
unchanged); 25 pages, 5 figure
Halo bias in Lagrangian Space: Estimators and theoretical predictions
We present several methods to accurately estimate Lagrangian bias parameters
and substantiate them using simulations. In particular, we focus on the
quadratic terms, both the local and the non local ones, and show the first
clear evidence for the latter in the simulations. Using Fourier space
correlations, we also show for the first time, the scale dependence of the
quadratic and non-local bias coefficients. For the linear bias, we fit for the
scale dependence and demonstrate the validity of a consistency relation between
linear bias parameters. Furthermore we employ real space estimators, using both
cross-correlations and the Peak-Background Split argument. This is the first
time the latter is used to measure anisotropic bias coefficients. We find good
agreement for all the parameters among these different methods, and also good
agreement for local bias with ESP theory predictions. We also try to
exploit possible relations among the different bias parameters. Finally, we
show how including higher order bias reduces the magnitude and scale dependence
of stochasticity of the halo field.Comment: 13 pages, 12 figure
Intensity mapping with neutral hydrogen and the Hidden Valley simulations
This paper introduces the Hidden Valley simulations, a set of
trillion-particle N-body simulations in gigaparsec volumes aimed at intensity
mapping science. We present details of the simulations and their convergence,
then specialize to the study of 21-cm fluctuations between redshifts 2 and 6.
Neutral hydrogen is assigned to halos using three prescriptions, and we
investigate the clustering in real and redshift-space at the 2-point level. In
common with earlier work we find the bias of HI increases from near 2 at z = 2
to 4 at z = 6, becoming more scale dependent at high z. The level of
scale-dependence and decorrelation with the matter field are as predicted by
perturbation theory. Due to the low mass of the hosting halos, the impact of
fingers of god is small on the range relevant for proposed 21-cm instruments.
We show that baryon acoustic oscillations and redshift-space distortions could
be well measured by such instruments. Taking advantage of the large simulation
volume, we assess the impact of fluctuations in the ultraviolet background,
which change HI clustering primarily at large scales.Comment: 36 pages, 21 figures. Simulations available at
http://cyril.astro.berkeley.edu/HiddenValley/ Minor changes in HI
normalization described in footnote of section
- …